Task Motivation and Engagement in Second Language Acquisition: A Structural Equation Modelling Analysis
ABSTRACT The last two decades have witnessed a growing interest among scholars in exploring the conceptualization, operationalization, and measurement of second language (L2) task engagement in diverse educational settings. However, the concept is still not clearly defined and remains complex. Clarifying this complexity is crucial for developing a valid theory of task engagement in second language acquisition (SLA). One prominent issue in capturing the complexity of L2 task engagement is its intricate relationship with task motivation. To address this issue, a structural equation modelling (SEM) analysis was conducted to investigate whether and to what extent task motivation, characterised by intrinsic motivation, identified regulation, external regulation, and amotivation, correlates with task engagement—behavioural, cognitive, emotional, social, and agentic—in Iran, where English is considered a foreign language (EFL). The findings suggested that intrinsic motivation and identified regulation were strongly linked to learner engagement in L2 tasks. In contrast, external regulation had weaker correlations, indicating that intrinsic factors were more important than extrinsic pressures and rewards in predicting task engagement in L2 learning. Overall, the findings suggested that L2 learners' intrinsic motivation and their perception of the importance and value of the task determined the extent and quality of their task engagement.
Authors
- Javad Zare (ORCID: https://orcid.org/0000-0003-1069-4862)
- Ali Derakhshan (ORCID: https://orcid.org/0000-0002-6639-9339)
- Fatemeh Ranjbaran (ORCID: https://orcid.org/0000-0001-9326-4475)
Institutions
- Golestan University (IR)
- Kosar University of Bojnord (IR)
- Sultan Qaboos University (OM)
Publication Details
- Journal
- European Journal of Education
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1111/ejed.70353
- Citations
- 1
- Primary Topic
- EFL/ESL Teaching and Learning
- Type
- article
- Field-Weighted Citation Impact
- 10.42